{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df=pd.read_csv('./log.txt',header=None,sep='\\t')\n",
    "df.columns=['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at' ]\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>160089</th>\n",
       "      <td>11941212</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>1036.43</td>\n",
       "      <td>61.27</td>\n",
       "      <td>482.48</td>\n",
       "      <td>207.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-09 00:18:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171922</th>\n",
       "      <td>12852726</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>13</td>\n",
       "      <td>2738.90</td>\n",
       "      <td>88.02</td>\n",
       "      <td>479.09</td>\n",
       "      <td>210.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-22 15:21:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89063</th>\n",
       "      <td>6769924</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>427.63</td>\n",
       "      <td>128.35</td>\n",
       "      <td>170.75</td>\n",
       "      <td>142.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-02-13 00:18:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52494</th>\n",
       "      <td>4339923</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>296.07</td>\n",
       "      <td>296.07</td>\n",
       "      <td>296.07</td>\n",
       "      <td>296.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 01:28:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174864</th>\n",
       "      <td>13076503</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>10</td>\n",
       "      <td>2113.40</td>\n",
       "      <td>135.82</td>\n",
       "      <td>313.73</td>\n",
       "      <td>211.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-25 20:21:16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                     api  count  res_time_sum  res_time_min  \\\n",
       "160089  11941212  /front-api/bill/create      5       1036.43         61.27   \n",
       "171922  12852726  /front-api/bill/create     13       2738.90         88.02   \n",
       "89063    6769924  /front-api/bill/create      3        427.63        128.35   \n",
       "52494    4339923  /front-api/bill/create      1        296.07        296.07   \n",
       "174864  13076503  /front-api/bill/create     10       2113.40        135.82   \n",
       "\n",
       "        res_time_max  res_time_avg  interval           created_at  \n",
       "160089        482.48         207.0        60  2019-05-09 00:18:57  \n",
       "171922        479.09         210.0        60  2019-05-22 15:21:13  \n",
       "89063         170.75         142.0        60  2019-02-13 00:18:08  \n",
       "52494         296.07         296.0        60  2019-01-01 01:28:56  \n",
       "174864        313.73         211.0        60  2019-05-25 20:21:16  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179496, 9)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                int64\n",
       "api              object\n",
       "count             int64\n",
       "res_time_sum    float64\n",
       "res_time_min    float64\n",
       "res_time_max    float64\n",
       "res_time_avg    float64\n",
       "interval          int64\n",
       "created_at       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   api           179496 non-null  object \n",
      " 2   count         179496 non-null  int64  \n",
      " 3   res_time_sum  179496 non-null  float64\n",
      " 4   res_time_min  179496 non-null  float64\n",
      " 5   res_time_max  179496 non-null  float64\n",
      " 6   res_time_avg  179496 non-null  float64\n",
      " 7   interval      179496 non-null  int64  \n",
      " 8   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop('api',axis=1) #优化内存，指定删除axis,指定删除列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  2019162542      8       1057.31         88.75        177.72         132.0   \n",
       "1      162644      5        749.12        103.79        240.38         149.0   \n",
       "\n",
       "   interval           created_at  \n",
       "0        60  2018-11-01 00:00:07  \n",
       "1        60  2018-11-01 00:01:07  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   count         179496 non-null  int64  \n",
      " 2   res_time_sum  179496 non-null  float64\n",
      " 3   res_time_min  179496 non-null  float64\n",
      " 4   res_time_max  179496 non-null  float64\n",
      " 5   res_time_avg  179496 non-null  float64\n",
      " 6   interval      179496 non-null  int64  \n",
      " 7   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=179496, step=1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index=df['created_at'] #设置时间为索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2018-11-01 00:00:07', '2018-11-01 00:01:07', '2018-11-01 00:02:07',\n",
       "       '2018-11-01 00:03:07', '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "       '2018-11-01 00:06:07', '2018-11-01 00:07:07', '2018-11-01 00:08:07',\n",
       "       '2018-11-01 00:09:07',\n",
       "       ...\n",
       "       '2019-05-30 23:01:21', '2019-05-30 23:02:21', '2019-05-30 23:03:21',\n",
       "       '2019-05-30 23:04:21', '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "       '2019-05-30 23:07:21', '2019-05-30 23:08:21', '2019-05-30 23:09:21',\n",
       "       '2019-05-30 23:10:21'],\n",
       "      dtype='object', name='created_at', length=179496)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index=pd.to_datetime(df.created_at)  #将字符串索引转为时间格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=179496, freq=None)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop('interval',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   count         179496 non-null  int64  \n",
      " 2   res_time_sum  179496 non-null  float64\n",
      " 3   res_time_min  179496 non-null  float64\n",
      " 4   res_time_max  179496 non-null  float64\n",
      " 5   res_time_avg  179496 non-null  float64\n",
      " 6   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(2), object(1)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop('id',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   count         179496 non-null  int64  \n",
      " 1   res_time_sum  179496 non-null  float64\n",
      " 2   res_time_min  179496 non-null  float64\n",
      " 3   res_time_max  179496 non-null  float64\n",
      " 4   res_time_avg  179496 non-null  float64\n",
      " 5   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 8.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count   res_time_sum   res_time_min   res_time_max  \\\n",
       "count  179496.000000  179496.000000  179496.000000  179496.000000   \n",
       "mean        7.175909    1393.177832     108.419626     359.880374   \n",
       "std         4.325160    1499.486073      79.640693     638.919827   \n",
       "min         1.000000      36.550000       3.210000      36.550000   \n",
       "25%         4.000000     607.707500      83.410000     198.280000   \n",
       "50%         7.000000    1154.905000      97.120000     256.090000   \n",
       "75%        10.000000    1834.117500     116.990000     374.410000   \n",
       "max        31.000000  142650.550000   18896.640000  142468.270000   \n",
       "\n",
       "        res_time_avg  \n",
       "count  179496.000000  \n",
       "mean      187.812208  \n",
       "std       224.464813  \n",
       "min        36.000000  \n",
       "25%       144.000000  \n",
       "50%       167.000000  \n",
       "75%       202.000000  \n",
       "max     71325.000000  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7wPIk5yf5BWAtsKPPc5Kkd5WBOGRUVRNJPgn8C7AA+FJV7T2OVR31cNIQspfBM1/6AHsZVH3tZSBOKkuS+m9QDhlJkvrMQJAkAfMoEObTR18k2Z9kT5Ink+zu93x6leRLSQ4l+WFX7YwkDyd5tl2f3s859uoovXw2yY/adnkyyUf7OcdeJDkvybeSPJ1kb5IbW33otssMvQzjdnlvkseTfL/18rlW7+t2mRfnENpHX/wnXR99AXxsWD/6Isl+YGVVDdWbbZL8LjAO3F1VF7fa3wKvVNWWFtSnV9Vf93OevThKL58Fxqvq7/o5t2ORZAmwpKq+l+T9wBPAVcB1DNl2maGXP2P4tkuAxVU1nuQU4DvAjcCf0sftMl/2EP7voy+q6n+AyY++0ElUVd8GXplSXgNsa7e30fkFHnhH6WXoVNXBqvpeu/068DRwLkO4XWboZehUx3i7e0q7FH3eLvMlEM4FXui6f4Ah/UFpCvjXJE+0j+sYZiNVdRA6v9DAOX2ez4n6ZJIftENKA3+YpVuSZcCHgF0M+XaZ0gsM4XZJsiDJk8Ah4OGq6vt2mS+B0NNHXwyRD1fVbwF/CNzQDl+o/+4AfhW4BDgI3NLf6fQuyfuArwGfqqqf9ns+J2KaXoZyu1TV21V1CZ1PZrgsycX9ntN8CYR59dEXVfViuz4EfIPOIbFh9VI79jt5DPhQn+dz3KrqpfZL/HPgHxiS7dKOUX8N+HJVfb2Vh3K7TNfLsG6XSVX1E2AMWE2ft8t8CYR589EXSRa3E2YkWQz8AfDDmUcNtB3AunZ7HfBAH+dyQiZ/UZs/YQi2Szt5eSfwdFV9vuuhodsuR+tlSLfL2Uk+2G4vAj4C/Ad93i7z4lVGAO2lZl/g/z/6YnOfp3RckvwKnb0C6Hy0yFeGpZckXwVG6XyE70vAZ4B/BrYDvwQ8D1xTVQN/svYovYzSOSxRwH7gLyeP9w6qJL8D/BuwB/h5K3+azrH3odouM/TyMYZvu/wGnZPGC+j8Y769qv4myZn0cbvMm0CQJJ2Y+XLISJJ0ggwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSp+V9t69IhYiTk4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist()#初步分析count,直方图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist(bins=30)  #找出大多数接口1分钟调用次数\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#切出一天数据，绘制一天时段的接口调用情况(折线图)\n",
    "df['2019-5-1']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:00:00</th>\n",
       "      <td>4.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 01:00:00</th>\n",
       "      <td>2.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 02:00:00</th>\n",
       "      <td>1.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 05:00:00</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 07:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 08:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 09:00:00</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 10:00:00</th>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 11:00:00</th>\n",
       "      <td>1.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 12:00:00</th>\n",
       "      <td>3.298246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 13:00:00</th>\n",
       "      <td>6.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:00</th>\n",
       "      <td>10.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 15:00:00</th>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 16:00:00</th>\n",
       "      <td>9.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 17:00:00</th>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:00:00</th>\n",
       "      <td>6.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:00:00</th>\n",
       "      <td>9.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:00:00</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 21:00:00</th>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 22:00:00</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:00:00</th>\n",
       "      <td>5.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count\n",
       "created_at                    \n",
       "2019-05-01 00:00:00   4.428571\n",
       "2019-05-01 01:00:00   2.272727\n",
       "2019-05-01 02:00:00   1.833333\n",
       "2019-05-01 03:00:00        NaN\n",
       "2019-05-01 04:00:00        NaN\n",
       "2019-05-01 05:00:00   2.000000\n",
       "2019-05-01 06:00:00        NaN\n",
       "2019-05-01 07:00:00        NaN\n",
       "2019-05-01 08:00:00        NaN\n",
       "2019-05-01 09:00:00   1.000000\n",
       "2019-05-01 10:00:00   1.400000\n",
       "2019-05-01 11:00:00   1.604651\n",
       "2019-05-01 12:00:00   3.298246\n",
       "2019-05-01 13:00:00   6.866667\n",
       "2019-05-01 14:00:00  10.483333\n",
       "2019-05-01 15:00:00  12.333333\n",
       "2019-05-01 16:00:00   9.916667\n",
       "2019-05-01 17:00:00   7.666667\n",
       "2019-05-01 18:00:00   6.783333\n",
       "2019-05-01 19:00:00   9.850000\n",
       "2019-05-01 20:00:00  11.000000\n",
       "2019-05-01 21:00:00  10.416667\n",
       "2019-05-01 22:00:00   8.000000\n",
       "2019-05-01 23:00:00   5.083333"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用count重采样，用一个小时进行采样\n",
    "df2=df['2019-5-1']\n",
    "df2=df2[['count']].resample('1H').mean()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3))#单位为英寸\n",
    "df2['count'].plot(kind='bar')\n",
    "plt.xticks(rotation=60)#文字旋转\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LwX+2iS5mgL+NiDMppXLn/wKpOV5GKEmZcgpFkjJlgEtSpgxwScqUAS5JmTLAJSlTBrgkZcoAl6RMGeCSlKn/A6+SRiLXU0nxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析有没有异常时间段，访问接口过于频繁\n",
    "df['2019-5-1'][['count']].boxplot(showmeans=True,meanline=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 20:47:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3117.20</td>\n",
       "      <td>84.90</td>\n",
       "      <td>260.82</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-01 20:47:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:03:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3706.20</td>\n",
       "      <td>78.12</td>\n",
       "      <td>321.47</td>\n",
       "      <td>176.0</td>\n",
       "      <td>2018-11-01 21:03:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:13:09</th>\n",
       "      <td>24</td>\n",
       "      <td>4602.03</td>\n",
       "      <td>76.31</td>\n",
       "      <td>391.12</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2018-11-01 21:13:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02 21:34:11</th>\n",
       "      <td>30</td>\n",
       "      <td>4610.15</td>\n",
       "      <td>72.49</td>\n",
       "      <td>463.41</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2018-11-02 21:34:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 14:20:13</th>\n",
       "      <td>21</td>\n",
       "      <td>3113.93</td>\n",
       "      <td>74.29</td>\n",
       "      <td>266.20</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-03 14:20:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:33:21</th>\n",
       "      <td>27</td>\n",
       "      <td>6456.64</td>\n",
       "      <td>99.65</td>\n",
       "      <td>978.91</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2019-05-30 21:33:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:43:21</th>\n",
       "      <td>21</td>\n",
       "      <td>6371.84</td>\n",
       "      <td>65.98</td>\n",
       "      <td>1175.37</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2019-05-30 21:43:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:47:21</th>\n",
       "      <td>21</td>\n",
       "      <td>3992.83</td>\n",
       "      <td>87.83</td>\n",
       "      <td>440.88</td>\n",
       "      <td>190.0</td>\n",
       "      <td>2019-05-30 21:47:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:53:21</th>\n",
       "      <td>24</td>\n",
       "      <td>8467.02</td>\n",
       "      <td>120.22</td>\n",
       "      <td>1511.17</td>\n",
       "      <td>352.0</td>\n",
       "      <td>2019-05-30 21:53:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:17:21</th>\n",
       "      <td>21</td>\n",
       "      <td>4926.35</td>\n",
       "      <td>85.01</td>\n",
       "      <td>826.90</td>\n",
       "      <td>234.0</td>\n",
       "      <td>2019-05-30 22:17:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>746 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 20:47:09     21       3117.20         84.90        260.82   \n",
       "2018-11-01 21:03:09     21       3706.20         78.12        321.47   \n",
       "2018-11-01 21:13:09     24       4602.03         76.31        391.12   \n",
       "2018-11-02 21:34:11     30       4610.15         72.49        463.41   \n",
       "2018-11-03 14:20:13     21       3113.93         74.29        266.20   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-30 21:33:21     27       6456.64         99.65        978.91   \n",
       "2019-05-30 21:43:21     21       6371.84         65.98       1175.37   \n",
       "2019-05-30 21:47:21     21       3992.83         87.83        440.88   \n",
       "2019-05-30 21:53:21     24       8467.02        120.22       1511.17   \n",
       "2019-05-30 22:17:21     21       4926.35         85.01        826.90   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-01 20:47:09         148.0  2018-11-01 20:47:09  \n",
       "2018-11-01 21:03:09         176.0  2018-11-01 21:03:09  \n",
       "2018-11-01 21:13:09         191.0  2018-11-01 21:13:09  \n",
       "2018-11-02 21:34:11         153.0  2018-11-02 21:34:11  \n",
       "2018-11-03 14:20:13         148.0  2018-11-03 14:20:13  \n",
       "...                           ...                  ...  \n",
       "2019-05-30 21:33:21         239.0  2019-05-30 21:33:21  \n",
       "2019-05-30 21:43:21         303.0  2019-05-30 21:43:21  \n",
       "2019-05-30 21:47:21         190.0  2019-05-30 21:47:21  \n",
       "2019-05-30 21:53:21         352.0  2019-05-30 21:53:21  \n",
       "2019-05-30 22:17:21         234.0  2019-05-30 22:17:21  \n",
       "\n",
       "[746 rows x 6 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['count']>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x170a8293128>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0Qk8/VMzmB4s2x32YWt/AcF0+/QOfL9ds51uPf3LAjbHkIib6Rs6xqnxP4HVl2n368UlyrL32eu6dHFD9X74+lxkrylmycXfCx0i2p54DlzElJLty1kp3lazZIlLqlnUQkQkistT9395X/w4RWSYii0Xk7GSNTxa7CXKTVVsraNmkEMhc9E6sCdeiCZfXiNRz7yRsVfaQDa6VXHWTxUsqevqnq+oQVR3qvr8dmKSq/YBJ7ntEZBBwGTAYOAd4REQKU3B+w6jH6vIKDu3cGoD9WRan75edsW8vYF9V/UZJ67l3MksmRNGWSGx80uHeuQh41n39LHCxr/wlVa1U1RXAMmBYGs4fN8s272psE4wUUV1Ty7pte+l/UCsgcz795WWxuS38wvrEtBU8PX1l/e0h6maqh5rO06Ti2PEe4tPlW7nwbx8FGn7r6DskK/oKjBeRWSIy2i3rrKobANz/B7nl3YE1vn3XumWNhncTfLq8vDHNMFLIhh37qK5V+rs9/fTPyHVuovfnR8+PDw2Fq6a2vn217vH8cf/xaFVNrSYcKpoLmvjJV1vZ7q5idserc5mzdgdrtlVE2Ss0z3+ykqWbcq9DmKzoD1fVY4BzgRtF5NQIdUM92IW8z0RktIiUikhpWVlZkiYa+YQXrnloZ6+nn5xP//Bfv89t//0yabs8Yu1t+n8s8fRQz3lwKof+8t2IdVZu2cOUJQ1/V+l8ogjl1km1p2dfVQ2XP/Ep33tmZtLHUlXufmM+F/19egosyy6SEn1VXe/+3wy8huOu2SQiXQHc/17c2lqgp2/3HsD6MMcdp6pDVXVoSUlJwvZNWLCJX70xL/C+fM9++tz+dsLHM7IfL3Ln0BJX9JOM3tldWc3LpWvDbk+5Tvp9+gkce+nm6G6mEX/+kKufmhHu1Gkh1s8SObVy5IN4T0mLNtTvnXuNSzzXc035XgAq9udeiGnCoi8iLUWktfcaGAXMA94ErnarXQ284b5+E7hMRJqKSF+gH9DwzkshP3iulOc+qcupviToUS2X4qANh9VbK2hSWEC3ds1pUliQuTj9GIkW4lkXvZP5Gajm867j1PsmA1BUkL6R5xH3Teau1+em7fjhSKan3xn4SES+xBHvt1X1PeBe4CwRWQqc5b5HVecDLwMLgPeAG1U195pRo1FZXV5Bjw7NKSwQmhYVZCC1cpz1o4VshtieKfE/0NMchLu2+6pqOfehacxYGf/YXVFh+kR/5dYK/vnp6rQdPxwJJ1xT1eXAUSHKtwIjw+wzFhib6DkNIxqrtlbQu0MLAJoWp7+nn+recV3IptRl2cyQFme6px/qdJFMiGZe8HbPHbR08y4WbtjJb/43Pw7rHIoLo/eL91RWM3NlOSMGHBQoe2XWWkTg0mN6xH3OdJPXM3LrZjwe2D0cw0FVWV1eQS9P9IsK0z4jN16Cb7Xg7JyBrJ2+usnenn94dyHPTF8Rtd7Au99L7kQRCCnwoZ5qkviwUfdN4NBFBcLv3lrAii3OWFFtrVJbW/9At/13Dtc8PZNVW+tmgt/yny/5+cupCwBIJXkt+kZuUb5nP7srq+nVsSVAhtw7caZhCKofLFQaCNlMzi4/j09Zzpj/LYipbrrnBET7XMmcvTbMzl7Dmsixt1VU8eRHK/je0zPYta+Ksx+cSv+76kdHLXMHz/dWHRjeahN9I2fwUip77p0mRY0zkBtJOGPvjEoDkSrbVcnYtxdQE07dUkC6Dh36moQcwOCcB6fy05e+CHGMRM6RmoZs5dYKjhgznqWbd1MddJECcyvSslJy6jHRx6J3coWA6Hf0fPqFSaVhCH6MD0VoF0WE+jEezwnZrD8j987X5vLEtBVMW5qauSuhxLC6NnONZLhB60Ubd/HG7JDR3HEfLxypXC3LO22kQJ+352yIedZ2ujHRj0BldQ3PfrwyrT0rI3V4E7N6Bnz6ybl3gnt0sRJxMDKqMvlDNusfr6omtekEQt3XNbWKqrI2aBZrdU0t33j0Y6Yv25KakxO7nz9WasP29L3/ddvveWdR0k8AG3bsZd32vb5Z1OHr3vjC55xx/5Skzpcq8kL0wz/2Rd7vsQ+X8+s35/PKrPCTc4zsYdXWCjq3aUqzYiePX9Mk3TvhRMSPv4Y38S/U/fZy6RqG/n5iA/dJg4HcED39YHGOZRzhgQlL2FNZHbFOTcievvL09JWc/MfJLNywM1C+Zfd+Sldt4+cvz4567kjUXxym4fbIT0l1G8v37OeOV+fUS1gXfG01UB76oMl25k78wwcMv/cD34Q6c+9kDdG+23A32jY3h8ee/ZF/PEZ2sLp8D707tAy8TzZ6J5Gefm2thrzf7nh1Llt2V0bNi1MXvSOB49T5jGPn4UlLeWDCkii2NiyrqVE+Xb4VqL8CWToI1TjGesX/9N4iXpyxhjdmrwuU/e6t0IPV4b7GUI1eIizfsifstqenr2DDjr0pOU+qyFnRX+H7IiL12K56agbjpi4PuS2RH5vReKwur6CX688HL04/untn3fa9DP39hHr3DDgCGJWge+vgO99h0cadDap591IooRnz5nwmLthU73AidfsENz7hbudgEd28qzKi6eF6+h4PT1rK6OdKIx4jGUK7d2ITYu/a+Ku/+WXocQDvmMHin+rhi+uemck3H/u4Xtlv/reA7z+bvmuYCDkr+qf/+cPA67C+PpSpS8pYtz10S+ztV5DGqdhGathXVcOmnZWByB2ApjGmYXjry/Vs2b2fF2fUnx2Z6KDm+/M3NijzbsFQh3zm45V83xVXv3/YE6nqWBof4NmPV9Z7/78wIugRzqfvsWDDTsYviC17KMAdr87h/vGLI9apVWXjjn0Mv/eDenHtHrH2vT030dY9+3n+01UR64ZrcFPV0/dYubWCmSu3NSifv75hJ6AxSXhG7oFEoPcUQ91FG3cysEsbwL9eaeKiv6+qhiaFBdZwpJnPVzs/toY9/ejCXeTOugyO9InF5xvvYGS0hqRe+F+gp+/sE+0+nL1me8TtwYQU/QgdpGi8OMPJnH7LqAER670+ex3rtu/lX581TEGwdff+8DuGMOG+951G5oS+HcLu5n3M4O83VGfwyY9W8LcPloa3IQb+8O7CsNsmLdzEyMM6J3X8ZMnZnr6fwKNgDHXPeXBag1C5ZPR64N3vcedrmU+qlC8s2bSLm174nCv+8RmtmxZxbO/A6pyuTz+6e6fYza8SLMix+PRDhx2GJ9rDQ62vh+KdvsFAbgM3hdLn9rd5Pc4wx5CiXxNa3pNen1br/kdymyb6Wwn1VQWersIYHyok93dvLWBbRVVCNng8PiW0uxjg0Q/Dh4pu3rkv4bUQ4iFnRf+K43sFXof77c5bF/qxy/sxeD/QghA9rD2V1Tw0cWkgjC7ScV6auSZsHSMxFm/cxY3/+pyzH5zK5EWbuf60Q5hy2+n0aO/r6ccYvePlV6mqDnr8jyD6+6pqeHnmGqpCqHhSPX13s9DQpx+u75FoaGmtKjODkpCFsy+WSKZwbNldyWI3w62iacl6EilFTrjL0xi/y+ZNQq8Qu6OiimH3TOL3b4d/SkgVOeveKfR1z5dt3s2Qnu0a/Gj+MS10i1xVoxQVRn6kfXzqch6etJQOLYv57ol9whwnu/K+5AILN+zk4UlLeXfeRlo1LeKGEYfw/ZMPpn3LJg3qeqKvqhFdIwHRr4ndvfOHdxby7Cer6Ocuy+gn0n0TzWXkXzmr1tc7rn/80PvES02tsnjjrgZloQg+xcINO+nQsgmd2zRj2579tGtRHPY81/oWNbnwb9P5wSl9E7LXb0Lw1xmp4Qs3OHzvu4sCryv2V9OiSfrlsHlxaNHfuc95unhn7gYAbjrjUDq1apoWG3K2p+/vnY95M3R2vXD3yX73x183kNZQvAvd42/aGT5CwkQ/dSxYv5Prn5/FuQ9NY9rSLfz4jEP56Benc+vZA0MKPjgzcqHu+wyH596pCrohIgmJFxkTctES327/nrnaieV2iTZ46Hd9+MXqhc9Wh538E6khiRQNU1OrMYun/xyTF2/m3Iemcfw9k1i9tYKjfzeBJz9aEfY83kxpjxneYGcK49o37NgXdlssbeKgX72fMlsiMX7BJka4ufr9eJdi865Knvl4Jb9+I/6MoLGSsz39lTFEBkxcGDoyoSog+s4e+33REx8u3szRPdvTvqXTs/Fi+UMfJw3PsXnGvHU7eHjSUsYv2ETrpkX8ZGQ/rhvel7YRepYeTYvqBmibFoXuYYHfvRP/QG4o/Hvd/urceqITHInj9ytXVtcEBhuDV876d+kaSlo5jZtfyFWVhyaFH3iM9BlqVRvkiwlX3/80Md4XneStP/vBos0N9qmzMeymuPhq826KCwvo0LJJg/bie0+HXyIx22bUrwwx/yHYhZzOnFE5K/pfrK6LZPC+8lhvvuDp7t77HRVVXPP0TIb16cAVJzhjBtsjDPpYTz9x5q7dwUOTljJx4SZaNyvipyP7cW2MYu/hiX5ldS2tfeUL1u/klc/Xctf5hyEigR9c8PcVyf8eceZohI3Brhh/z3/AXXWpjSWobrg8QGu37Q07zyT4+FU1tYEnVHDEMDhIIVxPf0u9qJrIPfTqmtpARBSkLnPnd/7xGZ1aNaH0rrMi1ttbVcO+qpqAmy3bRD8UDS1Mn805K/rF/hVvAtE7sV1Irzfm/ejufXcRz0xfySs3nATAsrLdgd7hlt3OY/5v/7eA4/q059wjugaOk0yyr3xlztrtPDRxKZMWbaZNsyJ+dmZ/rhneh7bNYxd7D693H9xr+tbjn7C7spqbz+xH62bFAVEKdgNFdJtEuJciD+SG7+n78fv0Aeau28HOfS3q1Vm0cSdvz9kQ/mTUjxbq98t3GTmwbqGPWm3o3gn1macsKQusqbtpZ2WD+QzBfLFmO8f1qQuhbLCGQND/eNgSKaTTh99fH2s8fmOunx18H6SzoUpY9EWkJ/Ac0AWoBcap6kMiMgb4AeClArxTVd9x97kDuA6oAX6iqmlzpBUV+HoawPB7Pwg7CSuYqUvLeHr6ykCebICNO/fxpRsHraqBL+WzFeXsqazmpZmrWb99bz3Rt55+7Mxes52HJi5h8uIy2jYv5paz+nP18D60aRa/2Hs0LXZ7+kFhm7uDctJ4ohDseklFwjUJeh/8Y374g2Uhj1Fdqw3ixb20CN54wjkPTotqS/DTyiSfG6a6Njb3TqhF1D2u+MdnDcqCXRXBR/Q2R3Ppz1xZHjbB2/RlWyPu+3LpGlo3c+QtG3v6wU8/wTam0+RkevrVwC2q+rm7QPosEZngbvuLqv7ZX1lEBgGXAYOBbsBEEemfrnVy/Wtbbq+oilnwAX752ryI25X6j96ryyuo2F8TGLBatnk3s1aVc3Sv9mGOYHh8vnobD01cypQlZbRrUcytZw/gqhN70zoJsffwu3dC4emh9wP7qmw3q7fWpXJI2KcfYbdYjxlpNu1dr89jULc2MR0n0sIeNbXK3W/Uv9cTnYW80TeQGryYeHAjGyvffOyTkOWz12xvMDgcTMX+Gir2O5891hnNmST4HgmXoz8dJLNG7gZgg/t6l4gsBLpH2OUi4CVVrQRWiMgyYBgQ+ptNEv/altFukFjxTzDxfydrtzkNypryClSVf0xbzksz1/DC948HUrsKUq4wa1U5D05cyrSlW2jfopjbzhnAVSf2oVXT1Hkcw7l3PDyB8x6tN++q5NT7JrPy3vOBaFEx4c+bTMhmrFz6yMfRKwHDxk4Ku622tuG1SdQ+f9IxL1y6fM/+emMIqWLXvvgmT6U63QKQdLbRaGG3adT81Pj0RaQPcDTwGTAcuElErgJKcZ4GtuE0CJ/6dltLmEZCREYDowF69eoVqkpUitOwir138+zYW1UvWmGN26jsqqxme0UV89bvAKB0lROaZppfR+nKch6a5Ih9h5ZNuP3cgXz3hN60TKHYewR6+mF6u973GS31bqgZ2ZG0MR6ffmMSLuFaadCErUTYXVnNMb+bEL1iAoSLdQ9HTRoWhnn183XRK0WgwYD+geDT9xCRVsArwM2qulNEHgV+h9OY/Q64H7iW0NoX8pOp6jhgHMDQoUMT+vR+n36q8A+2+DP6+Z8kHpiwhCUbnbEAb7ZjrToTVNGWugsAAB4OSURBVEYN6szIwzpT0jo9ky6ymRkrynlo0hKmL9tKp1ZNuPO8gVx5Qu+0TogJ+PTD9PS9H1a4H1h1QPQb3rqxCokExV7GshpXpggVaFBTo0mnIahV5ZYIPeFkBnIB/lMa3/oW2Ti0Fi3PU1a6dwBEpBhH8P+lqq8CqOom3/YngLfct2uBnr7dewDxr4kWI2nQ/LBfhD9boD/jnxc2emSPtizZtIsPFm1GZC7H9mrPqMGdGTWoC306tWxwvFzi0+VbeWjiUj5ZvpVOrZpy1/mHccXxvcNOR08lTQqdc1SEWQ8hkG4jbE/f+WGGSpYX6wzQ4B9zNvX0f/VGw7GrVNhXXat8VRY+x7zH56vjSxDn8e/S+NInPDYlsaURD+/eJmyqlmQZ/Ov6MSwNF8pJH8lE7wjwJLBQVR/wlXd1/f0AlwDenfUm8IKIPIAzkNsPCB8WkCTpaCjD/R5CLTZxdK92AdEfc+Fgju7ZjkUbdzF+/ibGL9jIPe8s4p53FtG/cytGDerCqMGdOaJ72wNm9Z1ofPLVVh6cuITPVpRT0jqzYu/RvX1zWjYp5K7X59GyaRGn9Cuptz14IDcYbwAwlHsn0uBgpFvvhn/NimRyRlkUlIIBqLcSVaLU1GrEXnz2NHuRydRC51t3V3LR36fXK5uxopzrn5/FY989NuXnS6anPxz4LjBXRLxnuTuBy0VkCM53uxL4IYCqzheRl4EFOJE/N6Yrcsc5X+qPGe7RfEWI2b8XD+keEP0mhQWICId1bcNhXdvw0zP7sXZbBRMWbGL8/E08OuUr/jZ5GV3aNOOsQZ0ZNbgzx/ftSJOiAytLhqo6Yj9pKTNWlHNQ66b86muD+M7xvQJLGGaSDi2b8PqNw7nxhc+56qkZ3DjiUG4+s19guzeQG86945UXilBdU8v+mtqAOyrS4GBwPhs/2T5L++Z/JzdACU6DGKnv8kWCPfxM8sIPjuehicmlWI6VSWFmM78XYl2GVJBM9M5HhHbLvRNhn7HA2ETPGQ/p8ImFzTWujrB7k3uaFRdwdK92ge3FIVIA9mjfgu8N78v3hvdl2579fLBoM+MXbOQ/s9bw/KeraN2siDMGHsSoQV04bUBJSqNaUo2qMn3ZVh6atISZK7fRuU1TxlwwiMuGNY7Y++nXuTVv3Hgyv35zHn+bvIwZvkHKutWX6n+v+6traVJUEAg1LBDh+n/OYuLCzYHInkgpcD9bkfxAqJ9Lj+7Oq18kPnA4oHPrQJbLTHD5E5/GPdiaTZx/ZFdOOqQTC9bvTPl3GYrb/jsn7efwk71KkoVE+qG3bVFMmTtp5tCDWjGwS5tAQxAtkqh9yyZ8/dgefP3YHuzdX8NHy7Ywfv5GJi3azBuz19OksIDhh3Zk1OAujDzsIA5q3SylnytRVJVpS7fw0KSlzFq1jS5tmvHbiwbzraE9G13s/TRvUsifvnEUJxzckVv+82WgfMybC/jn949v0NPfu7+G4kLhVvfHuKuymokL6/fGMtljL0oyEi2R2czJEml+QNbjfrXXndyXhyctZee+3FojO2dF/+Yz+3H9Pz9P6TEjJUFq7xP9QV3b0KSogEHd2jB7zfaQPf1wNG9SyFmDOnPWoM7U1CqzVm1j/PyNvL9gI5NfnYsIHNOrPaMGdWbU4C70bYSBYFVlypIyHpq0lC9Wb6db22b87uLD+dbQHhETmzU2lx7Tg/I9+wM5yz9yZ3sG63dFVXXYWPtVW/fQrV1z9oQZHE4HRXHcP6GIZ2JirvPezadEncnsT289+f9GcPO/ZzNtaeiZwZH49QWD+N+X6xMesE4XB5bTOA7OObwrK+89n/ZxJOiKxoeLy8Jua9e8Lr3vL88bBMCQno6LJ1HffGGBMKxvB+762iCm3no67918Cj87sz+V1TX84d1FnP7nDznrgSn86b1FzF6zPe3hgKrK5EWbueSRj7nm6Zls3lnJ2EsOZ/KtI/juCb2zWvA9rju5LxcN6RZ4X1VT2yD8ck9lTaABD+a0+z7kuLETWR5DdEqqCJ7hGi/tWhRz9uDIS/S1aZb9/b+jeraLXgk4rX/dgP2tZw+goy/1docWodNw+/F7+zq2asoJB3eM3Ugfx/Zuz8OXH53QvukkZ0XfI5UpSj8KkwcEqLeIhJcJ8oKjunLCwR1oH8ONFg0RYWCXNvxkZD/e+vEpTL/9DMZcMIiS1k15fOpyLv77dE68dxJ3vT6XqUvKUprsTVWZtHATF/99Ot97ZiZbdlfyh0uPYPL/jeCK4w8MsfcQES4eUjcncHtFFQuCFq5etXVPxPzskTKrpoNk55y0aVbMt4b2jFhn2m1n1BvkzkYOKWnJz8/qH7HO14/pwWNX1kW89GjfnDN9a9L6n5oO6xo6lUXwU160RjdcY1RUUBDStTbzl2fy7SjfRzrJ/uY9STKV6TLUykHH9u7AS6NPTMv5urdrzjXD+3LN8L5sr3AGgics2MQrs9bxz09X07ppEacPPIhRgztzWv+ShHLZOGK/mYcmLWXuuh307NCcP379CC49pkdcLqtsw58LpnzPfj5ZXj9513XPlmbapIj4x4QKpGGI6cElLXn1Ryexu7Kak/84ud62X553GBcO6caKLeGfTFb84TxEhJvP7M/4+ZtYsKGuETyuT3tmeouepJnXbjiJWoVrnp5B/86tmbWq7rznH9mVO887jP/OCj8x660fn8zh3dsCsPye85iytIwR/Us474iugdh+/4p67/70FKBhds1fnDOw3vsrT+jN/eOXBAI1zj+ya73spk9cdSxbdu1n3vod9Gzfgpte+Jyte/ZTXCiBp/yiAgnMgWjTvIhrT+7Lv0vXcFTPdoFEjqGoqdV6NqeCnBf94/p0aPCjjpWLh3SLebHpVCQIS5R2LZpw6TE9uPSYHuyrqmH6si2Mn7+JiQs38eaXzkDwSYd2ZNSgLpw5KPpAsKoyYcEmHv5gKfPW7aRXhxb86RtHcsnR3Q9osffYtLOuF1+6qpxNOys5+dBOEZ/kkuHEgzs2uAfPGHhQxIVH/JzSr4TH3Zz5b950MoO7tWHCgk20a9GEg0taBpbV8+7BpkUF/Ov7x9OkqIAjezi90BLf0nslrZsG3FePXXlMvbkhkUItLziqG1OXlNG/cysqq2uZs3ZHTPbHipegcO6Ys4E6MX7h+8dz0qGdgNBRea2bFfHiD04ICD44E+pOH+CkkQ5uNCPx8g9P5OCS+ktgtmxaxJKx5zJ58Wa+9/RMrjqhN2/P2UBhgfDVPecBcFDrZoEkeG1bFLN1z35EnKi+Mw/rzHdP7B3IVtqksIABXVrz/HXDOLJHO476zfgGdhzRvS1z1+1gf3Vtyue25LzoP3H1UNZuq2Dn3mq+9biT2+38I7ry9tzIecg9vIvv5+lrjuN7z9RfqWdo7/YRl4zLFM2KCxl5mJPuoaZW+Xy1MxA8fsEm7nxtLr98HY7u2Y6z3Alhh/hu8NpaZfyCTTw8aSkLNuykd8cW3PeNI7k4R8Te48Ih3bjv/cVUVtcG1iQ95/AuaRP9Dq2aUHrXmTQrLuRwdyZm0xDjPHeeN5B73lnUoPzkfp04uFNLlm/ZQ4EIIsKowV0a1CssEJ64aii9O7agf+fW9bYVFAiv3zicvftrOPGQjrw1Zz2Du7VtEAhwWv8S5q/fScsmhfxoxCEc0aNdQKzOPOwg/urzUQ/9/US27K5ssMpXInRpE7ojcnj3NgHBh/qulp+O7IcIfP+Ug6OGND937TDGTV1OyyZFHFzSkkuGNEz7tfC350QU2NMHHMTKe89ns9tpCLc4TJPCuvQfIsI/rh5ab7vXyAZPFvTzn+sdD0Go+yRZcl70WzUtYmAXpwV+5yenUFAAK8r2xCT6Crx6w0nU1CoD7/atauTrLbx503A+X7WNswd34a0fn8zmXeF9wZmmsEA4rk8HjuvTgTvPO4wlm3YzYYHTAPzxvUX88b1FHFLSMhAF9NRHK1i0cRd9O7Xk/m8exUVDuiUdOZKNHNS6GdNuO51h90xi+rKtdGnTLDDoHo2fnHEokxZtZr47DlBYINz/zaMiTmrq0KJJoDc+/fYzeOvL9Xzn+F68O8+ZfHPF8b0Ye8kRzI3Qc/bcBNHc+2cNCj9g6/+MXzuyW8g6t4wawGXH9QqklwZY/PtzGD9/E187smu9up4gTfjZaRQWCNsq9geyf4777rEsK9vNdSf3ZfPOSs58YEq98bURA0r4cHEZV5/Ym/01ypUnNEys+NmdIwM58T288Y0je7TlZ1H8+35O7V/Cqe4A7we3jAhZJ9YedbTfxGn9S1i0cVfMobLv/OQUznu4fkRROkOec170/XiPXwM6tw64bpoUFQT8/n/59lHc9do89rh5uGvVmVgVfP39PrYBXVoHHqGdx8u2ZCMiwoAurRnQpTU3ndGP9dv3MnGhMyP4ianLqa5VDi5pyV++fRQXHJmbYu/Hv5j6iYd0DDkmE4r+XVozanAXvvbXjwACj/eRRP+6k/sGXndv15wfnnYIAFNuHcFp933Ipcf0AOCIHm354u6zONrNTnneEV14Z67TMFx2XE/G/G9B2B5xqigskHqCD06K6guOathI1PVClb6dWtGXlky99XQAenVswSh3a88OLZh0y2mB8YahvdvzzPeGRbWlc4jP+s2hPShdVc6YCwfH/qGiMP83Z8cVYRdt3sRt5wzk28f1pEf7FhHrecS6NkKqyCvR9xARvn1cL16fvZ7ObZoy9dbTA49cvTq05I/vLmLGynKuPD50Wmd/1sVQGRgPBLq1a85VJ/bhqhP7sKOiimVluxnSs13KB42yleLCAq4d3penpq/gjIEH0S7GCKveHVoyoEvrBuXTbjudU/40uUH5TacfGjapXu+OLQMzfD38jdHfv3NM4LU3aJ9NtGjq9Ib2V9e5OYIbDI8e7Vvw0ugTuGzcp0n9Zlo3K+aRK1KbjybetN7FUR63CgukwbgAwA0jDgk7oH731wZxeLc2tGle3MA1l2ryUvTBeTwEAj5Sj2N7t+fl6xtG3Ey65TRG3j8lsI/HgSr6ftq2KObY3vm3ytevLhjE6FMPpnOb2FNdH9Gj7knuKN/rnh3qi503btQqifj3bE++98h3juWp6StCNoKh8AZh05EBN5N4HaN4hzFuC4oK8uN/Gkw3eSv6LZsW8cgVxzAoTKxuMIeUtKJzm6Zs2llZz/eXJx3jnKVL2zoXwss/PDEw2O/nhhGHcMLBHev5lyf87FQ6tw3tannrxydTsb+Gt+du4AzfQuS5Rq+OLeJys3Rs6TSuh3fLThdorBQXCu1bFHPr2eFFPJvJW9EHOO+IrtEr+ejUyhH9Hu2bc1r/EqYsKcv63pgRO8P6dqB5cSF7q2p49tphlLRqyqfLt3JtiF5YvxCP4G/cOJyd+6oCoYPBrptYufnMfmzZHXpG8IHMgC6tefWGkzii+4Et+iLCF78aFb1iliLhwo6yhaFDh2ppaXZMllm7rYKpS7bwneN7sa+qhi27K2MerDEODNSXd8UwDmREZJaqDg0uz+uefrz0aN+C77iDu82KC03wcxATeyPXOcCHVAzDMIx4yLjoi8g5IrJYRJaJyO2ZPr9hGEY+k1HRF5FC4O/AucAgnKUVB2XSBsMwjHwm0z39YcAyVV2uqvuBl4CLMmyDYRhG3pJp0e8OrPG9X+uWGYZhGBkg06IfKjSiQcyoiIwWkVIRKS0rC79alWEYhhEfmRb9tYB/yZgeQIOE9ao6TlWHqurQkpLw6UcNwzCM+Mjo5CwRKQKWACOBdcBM4DuqOj/CPmXAqgRP2QlIT5L05DC74sPsig+zKz5y1a7eqtqg15zRyVmqWi0iNwHvA4XAU5EE390n4a6+iJSGmpHW2Jhd8WF2xYfZFR/5ZlfGZ+Sq6jvAO5k+r2EYhmEzcg3DMPKKXBf9cY1tQBjMrvgwu+LD7IqPvLIr67NsGoZhGKkj13v6RhjE0knmBPY9GvFyQIu+iHTNxpteRLqJSOxr8GUIETlCRH4BoFn0iCciXRrbhlCISOfGtiEUIjJARM6FrPsee4tI6IWlGxERSe9q8gnSWPp1QIq+iDQVkUeBKcA4Ebm0sW0CEJFWIvIA8C7wDxH5jlveqNdZHP4MvAAUiUhxY9rjISLNReRB4D0R+YuIZEUeJvd7/Avwrog8nmX31/3Ai0BsK7lnAPd7/AvOff+siPzILW/s+76liIwDfi0iHd2yRu8kNrZ+HZCiD1wIdFXV/sBbwG9FpH9jGiQi3YBncH6Mw4E3AK9XXdt4lgFQAnQFjlXVsapa1cj2eNwIlKjqEOB14B4RObQxDRKR7sDzOL+N83B+mH9qTJsARKQN8Cpwsqoeo6pvNLZNPn4CdFPVQcAY4GZo3Pve7d3/FjgZaA2c7tqUDU9GjapfB4zoi0gr31sFygDcm/894Ici0q4R7PIWS90B3KKqN6nqbqAz8LqIlLj1Mp3G2r+Ia1ugn6ruF5GzReT/ROTsTNrjs6uV+78QaI9z06OqU4A9OL2yxlxEdR/wD1X9qapuBF4GZovIkY1ok2fX88B8ABEZLiKjRKSf+74x1sYodM8rwBy3uBvwtog0yqrhIuItZ1cJPAqcCiwFjhWRQ9w6Ge/tZ5N+Zb3oi8ihIvIy8IyInC8iLYG9wE63dw1wH3AMMNjdJ+1farBdQLGqrhKRFiLyU+B2oCXOD2CQqtZm2K6n3evVAdgNTBeR3wK34QjIgyJyddDNmAm7nhWRr7nFu4DjReQot3FcBPQHDnb3ycT1GiAij4lIcwBV3Qp86KvS07VncbptiWLXfuADQEVkI3APcBYwRUQGZ/D+CtilqjVub3490EtEpgF/xPleJ4rIWZkSWBHpJyLP4bhLLgRaq+oyVd0CTAaa0Qi9/azUL1XN2j+cRukt4G6cvPuPAvcCTYG3cRZjaeLWHQP8t5Hs+jvwV3ebAP19dX8LTGgkux4B/uxu+yuOmB3lvv8G8F+cH0em7XoMGAsUu///A8x2v8/fAeMydL1OBmYAtcAvve8vqM4A4NVM2BPJLt91HAn8X9D99V4W2NUW56moi1t2I/BOhuz6LrAA+BFwLfAEcFVQnR8Af8FxcWbqe8xO/crUBUjwonUH/gkU+t7PAI4Hvgk8DQxztw10v+ziRrLrE+BC97144oHTS3wdaN5Idn2G84h7FDABuNZXfzKOL7ax7Brlvu8LdHBffx34mXcd02zXYcDhwKHAMpwEVcF1LgPuc1//ADgyA9cr2K4+vm3Ngur2w/H1N2ssu9z7vbsrqge7ZU1xOhkdM2DXKOAC3/s/Ate7r4vc/72Au4AbcJ7CT82AXVmpX1nt3lHVdcBQnMdY7/0jwG9U9T84GTvvEJFbcFbhWq4ZGKQMY9ejwM/c96qqKiInAk8BH6vq3ka0625V/RJnht8FInKH+yg+DyhvJLv+Dtzpvl+hquUicirwc9yFdtT9NaTRroU4K7ktw2kQfwsN/OMjgY4i8grwHRzXWFoJYddvXLtEVQPnF5GTgCeBT/3lmbbL/Z424jRAPxCRa3CSKs7EGetKt13jgfHiZPEF5zvq5m6rdv+vBloBv8dpyBvrvm90/UprixJHi9igF0xd63gN8JGvvB3OY+RxOD2MU4CHgCuzwK4XXXta4vwgvgC+lQV2/Rs4yX0/GLgFuCwL7HoRt8eF08NfipNqO+12+bZ5T2StcXqvI4O2v4szePqNbLELR7x+geMS+3YW2XUkTk/67UzdX2Hq/Qu4NKjsOGADcEUa7OoAtPFfI+qeMBpNv8Lam6kTRbhg9wL/A4523xcEbS/EGcC62Vf2LHB4NtsFDMlGu7L4erVrDLs829z/NwNvua8vd3+UI7LQriJ840ZZZFfaXJgx2lUAtABew4meE+BsoGka7bobWIjTUx8TbFtj/R4j/TX25Inv43wpS4FLoWFsr6rWALcCPxWRi0XkShy/YtpigJO0y9s+O8vsytbrpe727Y1hl0utu+1BYLiI7ADOxBGLD7PQrmJVXZJldp2BOw+wsexyy9q6f+dTN56VcrvEmfh1H849PAL4FXCziPRRXyRVY/weo5LpVgZ3wM593R5nycRTgceB89xy8dUpcP9fhOMymYozQcXsMruStstXty1O6NwcYLjZdcDadQGOoL4MnJIuu3CetkbgunHcsifwBUq4ZRm57+P6DBk7kXOT/AP4GMfvNzho20+Bh3F9Y9T5DtMdwWF25bFdvjoFpCEyx+zKuF0tgR+m2a4bcd1r7nUQnJn4kwly66b7vk/kL5PunTtw/FvX4bTcgVzRqroDJ+RRcOLHUfeKef/NLrMrHXb56tSq6hxSj9mVIbtEpEBV96jq4xmw62n3/LU4vf4qnFnA6/w7ZeC+j5u0i744eKFU/1LVhao6FtgvIr/xVZ2H01IeISK3isiP0jkzzewyu8yu3LJL05DrJ4xdv/fbpU6YZV+gWlXLRORSEbks1bakirSLvjpU48TNHuvbdANwg4i0d+tV4LTglwGjcWJW09ZKml1ml9lldqXKLpxY/BbipIL4Bc68hexEU+v3ao4z89MfsuQNZByDk2SouW/bE8BtWuczW45virnZZXaZXWZXltt1h/v6dmAT8INU25Xqv5T19EXkemAWMAw3DM+3rVBVPwcm4cxI81iMk6wJdfx1A1X1z6myyewyu8wusyvNdq1xX7+Pk5biiVTalRZS0Dq2xRlsWYjzZfi3+VvMvjj5OKbhTL+/DCfc69JkbTC7zC6zy+xqJLtSPlM73X/JXCxvmnEhzsSE2933JTjxq63d951x8oB/ipNVcQhOJrzxwNfT8CWaXWaX2WV2HZB2ZeLPi6GOGXck+173Aryjqu+LyCCctKZH4rSci3HiVh8H1gJnqOrDcZ0oTswus8vsMrsOVLsySpyto+D4tP4JXAFMxBnFFuBK4M84j0CFOI8/H1F/VmZhOlous8vsMrvMrgPVrkz/xXvR2uDMSPMefc4G/obr18KX2Agnv8ST7j4NkiOl+Ms0u8wus8vsOiDtyvRfXNE7qroTWImTLhRgOlAKnC4iXVS1EvDWZ70TqFDVnZrmBZLNLrPL7DK7DlS7Mk0iIZuvAUNEpKs6C4DPwZl+3NWdvfZTnMeiJar64xTaanaZXWaX2ZWrdmWMRET/I2ArbmupTvzqMKClOs9Fs4BzVXVMimw0u8wus8vsynW7MkZR9Cr1UdUNIvI6cK+ILMNZEm0f4C1L9lFqTTS7zC6zy+zKbbsySqKDATgruT8FLAJuSvQ4qf4zu8wus8vsOlDtysRf3HH6fkSk2Gk3nMWHswWzKz7Mrvgwu+LD7MoukhJ9wzAM48CiUdfINQzDMDKLib5hGEYeYaJvGIaRR5joG4Zh5BEm+oZhGHmEib5hGEYeYaJvGDEgIiNE5KQE9lspIp0S2O/OePcxjFgw0TfyDnchjXgZAcQt+klgom+khURufsPIekTkKuD/cBa5ngPUAOXA0cDnIvII8Hec5fEqgB+o6iIRuQC4C2flpK04i200B64HakTkSuDHONP3HwN6uae8WVWni0hH4EX3uDNwFuiIZOfrQE+gGfCQqo4TkXuB5iIyG5ivqlek4poYBtiMXCMHEZHBwKvAcFXdIiIdgAeATsBFqlojIpOA61V1qYgcD/xBVc8QkfbAdlVVEfk+cJiq3iIiY4Ddqvpn9xwvAI+o6kci0gt4X1UPE5GHgS2q+lsROR94CyhR1S1hbO2gquUi0hwn+ddpqrpVRHaraqt0XicjP7GevpGLnAH81xNaV1QB/uMKfiscV81/3HJwlskD6AH8W0S64vT2V4Q5x5nAIN/+bdzFN04FLnXP+7aIbIti609E5BL3dU+gH84ThmGkBRN9IxcRHLdOMHvc/wU4vfkhIer8FXhAVd8UkRHAmDDnKABOVNW99U7sNAIxPT67xz/TPU6FiHyI4+YxjLRhA7lGLjIJ+JbrX8d17wRQZ9m8FSLyTXe7iMhR7ua2wDr39dW+3XYBrX3vxwM3eW9ExGtApuKMAyAi5wLtI9jZFtjmCv5A4ATftio3C6RhpBQTfSPnUNX5wFhgioh8iePPD+YK4Dp3+3zgIrd8DI7bZxrg98P/D7hERGaLyCnAT4ChIjJHRBbgDPQC/AY4VUQ+B0YBqyOY+h5QJCJzgN8Bn/q2jQPmiMi/Yv3chhELNpBrGIaRR1hP3zAMI4+wgVzDSDPu2MKkEJtGqqpF6hgZxdw7hmEYeYS5dwzDMPIIE33DMIw8wkTfMAwjjzDRNwzDyCNM9A3DMPKI/wevGmBhNIO8jQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#某一天的响应时间，平均响应时间\n",
    "df['2019-5-1']['res_time_avg'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x170a746fc18>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1'][['res_time_avg']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:34:48</th>\n",
       "      <td>1</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.0</td>\n",
       "      <td>2019-05-01 00:34:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:49</th>\n",
       "      <td>17</td>\n",
       "      <td>19770.18</td>\n",
       "      <td>207.54</td>\n",
       "      <td>2974.52</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>2019-05-01 14:00:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:36:49</th>\n",
       "      <td>8</td>\n",
       "      <td>8799.92</td>\n",
       "      <td>96.59</td>\n",
       "      <td>3233.26</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>2019-05-01 18:36:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:09:49</th>\n",
       "      <td>6</td>\n",
       "      <td>7399.94</td>\n",
       "      <td>307.39</td>\n",
       "      <td>3153.02</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>2019-05-01 19:09:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:10:49</th>\n",
       "      <td>13</td>\n",
       "      <td>23595.60</td>\n",
       "      <td>206.20</td>\n",
       "      <td>4664.84</td>\n",
       "      <td>1815.0</td>\n",
       "      <td>2019-05-01 19:10:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:38:49</th>\n",
       "      <td>15</td>\n",
       "      <td>16169.25</td>\n",
       "      <td>142.47</td>\n",
       "      <td>3624.26</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>2019-05-01 20:38:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2019-05-01 00:34:48      1       1694.47       1694.47       1694.47   \n",
       "2019-05-01 14:00:49     17      19770.18        207.54       2974.52   \n",
       "2019-05-01 18:36:49      8       8799.92         96.59       3233.26   \n",
       "2019-05-01 19:09:49      6       7399.94        307.39       3153.02   \n",
       "2019-05-01 19:10:49     13      23595.60        206.20       4664.84   \n",
       "2019-05-01 20:38:49     15      16169.25        142.47       3624.26   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2019-05-01 00:34:48        1694.0  2019-05-01 00:34:48  \n",
       "2019-05-01 14:00:49        1162.0  2019-05-01 14:00:49  \n",
       "2019-05-01 18:36:49        1099.0  2019-05-01 18:36:49  \n",
       "2019-05-01 19:09:49        1233.0  2019-05-01 19:09:49  \n",
       "2019-05-01 19:10:49        1815.0  2019-05-01 19:10:49  \n",
       "2019-05-01 20:38:49        1077.0  2019-05-01 20:38:49  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df['2019-5-1']\n",
    "df2[df2['res_time_avg']>1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=df['2019-5-1'].resample('20T').mean()#重新取样，取20分钟的平均值 \n",
    "data[['res_time_sum','res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1':'2019-5-10']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  \n",
       "created_at                                                       \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekday']=df.index.weekday  #0代表周一\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  weekend  \n",
       "created_at                                                                \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3    False  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3    False  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07        3    False  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07        3    False  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07        3    False  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#判断是否为周末\n",
    "df['weekend']=df['weekday'].isin({5,6})\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对周末进行分组求平均值\n",
    "df.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  created_at\n",
       "False    0              3.239120\n",
       "         1              1.668388\n",
       "         2              1.162551\n",
       "         3              1.086705\n",
       "         4              1.155556\n",
       "         5              1.136364\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.000000\n",
       "         9              1.080000\n",
       "         10             1.239011\n",
       "         11             2.031690\n",
       "         12             4.195845\n",
       "         13             6.668042\n",
       "         14             8.260503\n",
       "         15             8.934448\n",
       "         16             8.466504\n",
       "         17             6.784996\n",
       "         18             6.717731\n",
       "         19             8.655913\n",
       "         20            10.536496\n",
       "         21            10.846906\n",
       "         22             9.034164\n",
       "         23             5.946834\n",
       "True     0              3.467782\n",
       "         1              1.741849\n",
       "         2              1.161826\n",
       "         3              1.050000\n",
       "         4              1.076923\n",
       "         5              1.333333\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.071429\n",
       "         9              1.144928\n",
       "         10             1.254111\n",
       "         11             1.992958\n",
       "         12             4.031889\n",
       "         13             6.905772\n",
       "         14             8.851321\n",
       "         15             9.858422\n",
       "         16             9.420550\n",
       "         17             7.334743\n",
       "         18             7.342150\n",
       "         19             9.270430\n",
       "         20            11.173609\n",
       "         21            11.695043\n",
       "         22            10.419916\n",
       "         23             7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获得周末什么时间段访问量\n",
    "df.groupby(['weekend',df.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level=0).plot()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
